matt harrison mattharrison

Hardware

Emacs keybindings everywhere
- P1 - AutoHotKeys with emacseverywhere
- M2 - Hammerspoon with https://github.com/justintanner/universal-emacs-keybindings
Zoom

	import pandas as pd

	url = 'http://atmenv.envi.osakafu-u.ac.jp/omu-content/uploads/sites/1215/2015/10/KyotoFullFlower7.xls'
	cherry_raw = pd.read_excel(url, skiprows=25, dtype_backend='pyarrow')

	def tweak_cherry(df):
	return (df
	#.query('~`Full-flowering date (DOY)`.isna()')
	.rename(columns={'AD': 'year',
	'Full-flowering date (DOY)': 'flowering_doy',

	from sklearn.metrics import roc_auc_score
	from hyperopt import hp, Trials, fmin, tpe

	from hyperopt import fmin, tpe, hp, STATUS_OK, Trials
	from sklearn.metrics import accuracy_score, roc_auc_score

	from typing import Any, Dict, Union
	import xgboost as xgb

	def hyperparameter_tuning(space: Dict[str, Union[float, int]],

	%matplotliblib inline
	import pandas as pd
	url = 'https://github.com/COVID19Tracking/covid-tracking-data/raw/master/data/states_daily_4pm_et.csv'
	df = pd.read_csv(url, parse_dates=['date', 'dateChecked'])

	(df
	[df.state == 'NC']
	.set_index('date')
	[['positive', 'hospitalized']]
	.fillna(0)

	def tweak_nyc(raw):
	def clean_col(val):
	return val.strip().replace(' ', '_')
	return (raw
	.rename(columns=clean_col)
	.assign(PrecipitationIn=pd.to_numeric(
	raw.PrecipitationIn.replace('T', '0.001')),
	Events=raw[' Events'].fillna(''),
	)
	.assign(PrecipitationCM=lambda df_:df_.PrecipitationIn*2.54)

	"""
	This is a module docstring. It must be at the TOP
	of the file.

	This is the markov module. You can create
	a markov chain like this:

	>>> m = Markov('ab')
	>>> m.predict('a')
	'b'